Florian Ingels

Dr Kareem Carr man: i wish to publish @kareem_carr Jan 21 reviewer 2: your paper is no good man: i'll do anything to improve reviewer 2: it's simple. you must read the work of the great scientist Pagliarini man: *bursts into tears* but i am Pagliarini Andre Pagliarini @apagliar Jan 21 a first: in rejecting an article I submitted to a journal, reviewer 2 noted I failed to engage the work of one Andre Pagliarini Jan 21, 2026 • 3:47 PM UTC

I just thought everyone should see this

DSB Program is out !
Seems incredible (as ever)
dsb-meeting.github.io/DSB2026/

Literally a publication for eight-year olds 40 years ago

Un super billet d'Arthur, qui met des mots sur des choses qu'on ressent forcément quand on est dans l'ESR, et qui les relie entre elles de manière très convaincante.

On ne manque pas de problèmes! C'est ça qui est chouette en interdisciplinaire. Je privilégie ceux qui me plaisent, bien sûr, mais c'est un mélange de plaisir / abordabilité / "rentabilité" (étant en postdoc je n'ai pas le luxe de traiter des sujets trop obscurs peu valorisables dans un dossier)

Il se trouve que je fais des maths appliquées (à la bioinformatique, en l'occurrence). Je cherche la petite bête, le petit coin où il faudrait un peu de maths pour aider les collègues, et après je me lance. De fait, parfois ce que je trouve aide les collègues, mais parfois non.

a man wearing overalls and a plaid shirt says it ain 't much but it 's honest work ALT: a man wearing overalls and a plaid shirt says it ain 't much but it 's honest work

Peu m'importe, au final, si ça atteint, ou non, des gens, tant que je n'ai pas à rougir de ce que je propose !

Mon idéal à moi, c'est de proposer un problème (à ma portée), de l'attaquer honnêtement et sincèrement, de proposer une solution aussi complète que possible, qui fait le tour du sujet (sans saucissonnage) et, lorsque je n'ai pas à rougir de ce que j'ai à disposition, alors je l'envoie.

Nicolas Sarkozy reçoit le “prix de littérature” de la FIFA Toute l'information selon des sources contradictoires.

Nicolas Sarkozy reçoit le “prix de littérature” de la FIFA https://www.legorafi.fr/2025/12/12/nicolas-sarkozy-recoit-le-prix-de-litterature-de-la-fifa/

Dans l'article, une application c'est la numérotation des permutations à n éléments : tu peux trouver le rang d'une permutation en calculant sa valeur en num. factorielle, et vice-versa. Pour les gens (comme moi) qui aiment l'énumération c'est super cool, en vrai.

On a dix chiffres (0, 1, 2, 3, 4, 5, 6, 7, 8 et 9) pour écrire tous les nombres. Vous trouvez pas ça dommage, vous de gâcher les grands chiffres (comme 9, le plus grand chiffre) pour écrire des nombres aussi petits que 9 ou 19 ?
Ça paraît idiot, mais… petit thread de maths !

Preprint Alert!
We present new strategies to accelerate large-scale document comparison using MinHash-like sketches.

A thread:

And now, for the 25th post, i.e. CHRISTMAS MORNING, the promised thread by Antoine :
bsky.app/profile/npma...

Preprint alert!

We introduce new ideas to revisit the notion of sampling with window guarantees, also known as minimizers.

A thread:

I found a flowchart which helps you navigate the IT landscape

His book "Mathematica" is a real eye-opener. David Bessis describes accurately and vividly the way we perceive and manipulate mathematical objects. Colleagues who read it also felt he put into words what they had not been able to formulate about the mathematical process. Highly recommended.

This kind of situation appear elsewhere in the simulations, but it happens randomly after Z2, so it is averaged over all 10^6 simulations and the effect is smoothed. That's why I was speaking of a border effect, as Y1/Z1 is very special by being the only window without prior dependencies

Yeah so basically Y1 is always a rescan, and Y2* (the second selected position) always follows a rescan, so Z2 = Y2* - Y1 is always the gap between a rescan and its successor.

There is some bias as the next minimizer after a rescan is more likely to be far as it follows the min over a whole window

And yes, the result is true when taking the limit to infinity. In the proof of Theorem 1, we establish the following
--- where E[tau_M] / (M-k+1) is the expected specific density of a random sequence of length M (and then M->infty)

You can derive actually an interval for a finite sequence from this

You are right in that eps_i is not *technically* a random variable. And yes, we want the average eps_i to be 0.

We chose to consider the eps_i as some realizations of an underlying random variable eps of mean 0, but it is only for the proof and not a big deal actually.

But as you can see, numerically with Monte Carlo, we obtain that E[Z_i] are somehow around (w+1)/2, so all good.

(and the values E[Z_i] - (w+1)/2 are, equivalently, somewhat around 0)

They're not, actually ! For instance, this is what we (formally) obtain for Z1 and Z2, with random minimizers. For w=10, E[Z1] = 5.5 whereas E[Z2] = 5.87 (there is a good reason for this, I can explain more if you're curious, but basically it is a border effect).

Where the Z_i's i.i.d then it would simply be E[Z1] but we didnt want to assume independence nor identical distribution, as to be as general as possible (also they are not iid in real life)

There are several way to define it actually, but you can think of it as E[E[Z_i]] where Z_i is the i-th gap. So, the average of the average gap.

I would also argue that n / # samples ≠ avg gap.

Take a sequence with n=6, and sample positions 1 and 3.

You get 3 ≠ ((1-0) + (3-1))/2 = 1.5

You need to account for the remaining bits after the last sample. And this implies going to infinity!

The only proper formal definition of density I found (the one I just gave) was in the GreedyMini paper. Other references, as far as I know, defines it informally, which is usually enough, but not when you want to claim a mathematical truth !

Well, usually when things seems trivial, one must be cautious. Since the density is defined as the limit of expected specific density when S tends to infinity, maybe there could have been a trick with the limit. Better safe than sorry, I would say

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I think I will leave my 🧵 here now, hopeful that you made it to the end (math can be scary sometimes). Thanks for tagging along and wait for @npmalfoy.bsky.social thread on the multiminimizer trick !

24/24

(just like an advent calendar, 24 !)

Basically, we think that d* is an interesting concept, tackling problems that are not covered by classical density. It seems also difficult to attack from a theoretical aspect (the NP-complete result + that awful formula for random minimizers).

This leaves plenty of work left to do !

23/

We also prove that applying the multiminimizer trick to filtering application like Needle (sorry again for hiding under the carper what a multiminimizer is) lead to a NP-complete problem - but we provided a good proof of concept greedy heuristic that works nicely.

22/